code: def test_masking(self):
      class BasicMaskedLayer(layers.Layer):
          def __init__(self):
              super().__init__()
              self.supports_masking = True
  
          def call(self, x, mask=None):
              assert mask is not None
              return x
  
      layer = BasicMaskedLayer()
      x = backend.numpy.ones((4, 4))
      x._keras_mask = backend.numpy.ones((4,))
      layer(x)
  
      layer(backend.numpy.ones((4, 4)), mask=backend.numpy.ones((4,)))
  
      class NestedInputMaskedLayer(layers.Layer):
          def __init__(self):
              super().__init__()
              self.supports_masking = True
  
          def call(self, x, mask=None):
              assert isinstance(x, list)
              assert len(x) == 2
              assert isinstance(mask, list)
              assert len(mask) == 2
              return x
  
      layer = NestedInputMaskedLayer()
      x1 = backend.numpy.ones((4, 4))
      x1._keras_mask = backend.numpy.ones((4,))
      x2 = backend.numpy.ones((4, 4))
      x2._keras_mask = backend.numpy.ones((4,))
      layer([x1, x2])
  
      layer(
          [backend.numpy.ones((4, 4)), backend.numpy.ones((4, 4))],
          mask=[backend.numpy.ones((4,)), backend.numpy.ones((4,))],
      )
  
      class PositionalInputsMaskedLayer(layers.Layer):
          def __init__(self):
              super().__init__()
              self.supports_masking = True
  
          def call(self, x1, x2, x1_mask=None, x2_mask=None):
              assert x1_mask is not None
              assert x2_mask is not None
              return x1 + x2
  
      layer = PositionalInputsMaskedLayer()
      layer(x1, x2)
      layer(x1=x1, x2=x2)
  
      class PositionalNestedInputsMaskedLayer(layers.Layer):
          def __init__(self):
              super().__init__()
              self.supports_masking = True
  
          def call(self, x1, x2, x1_mask=None, x2_mask=None):
              assert isinstance(x1, tuple)
              assert x1_mask is not None
              assert x2_mask is not None
              assert isinstance(x1_mask, tuple)
              return x1[0] + x1[1] + x2
  
      layer = PositionalNestedInputsMaskedLayer()
      x1_1 = backend.numpy.ones((4, 4))
      x1_1._keras_mask = backend.numpy.ones((4,))
      x1_2 = backend.numpy.ones((4, 4))
      x1_2._keras_mask = backend.numpy.ones((4,))
      x2 = backend.numpy.ones((4, 4))
      x2._keras_mask = backend.numpy.ones((4,))
      layer((x1_1, x1_2), x2)
      layer(x1=(x1_1, x1_2), x2=x2)
  
imports: import numpy
  import pytest
  from keras import backend
  from keras import layers
  from keras import metrics
  from keras import models
  from keras import ops
  from keras import testing
label_desc: Use image node_id 12 for calling the LayerTest obj's underlying member method code with example usage: obj.test_masking() and returns: x, x, unknown, unknown